Geospatial investigation on transitional (quiescence to surge initiation) phase dynamics of Monacobreen tidewater glacier, Svalbard

Banerjee, Debangshu; Garg, Vaibhav; Thakur, Praveen K.

doi:10.1016/j.asr.2021.08.020

Cited by 6 publications

(1 citation statement)

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“…Since most of the glaciers in these regions are hardly accessible without substantial expenses, remote sensing plays a vital role in studying their behaviour. Studies using remote sensing techniques to distinguish surge-type glaciers from others are oftentimes either based on morphological and more qualitative attributes of a surge, or quantitative approaches such as elevation or velocity changes [11][12][13][14][15]. Due to snow or cloud coverage, no visible terminus advance during a surge or missing illumination by the sun in arctic winters, the usage of radar satellite imagery and a quantitative approach may be advantageous over optical image sources [12,13].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard

et al. 2023

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Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase.

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